1. Field of the Invention
The present invention generally relates to a computer implemented and/or assisted process for controlling drug or health care spending and/or use while improving or maintaining the quality of care in a patient population. More particularly, the present invention relates to a computer implemented and/or assisted process for ensuring and/or designing appropriate patient care, through the selection and/or collection of extensive information on a patient's use of medication(s), medical history, and/or satisfaction, as well as the involvement of both the patient and physician in the decision-making process. The patient may optionally be, for example, using multiple medications for treatment of multiple diseases in the computer implemented and/or assisted process.
2. Background of the Related Art
Health care costs currently represent a significant portion of the United States Gross National Product, and continue to rise at an exceptional pace. A significant portion of these increased costs represents an inability to appropriately coordinate or target the appropriate medications and/or treatment for individual patients. Accordingly, we have determined that many people are deprived of access to services which can coordinate the most needed medical care and information.
Coupled with the inability to prescribe the appropriate medication and/or treatment, many people delay in obtaining, or are prevented from seeking, medical attention because of cost, time constraints, or inconvenience. If the public had universal, unrestricted and easy access to medical information, many diseases could be prevented. Similarly, the early detection and treatment of numerous diseases could keep many patients from reaching the advanced stages of illness. These advanced stages of illness are a significant part of the costs attributed to our nation's health care system. It is obvious that the United States, and the world is facing health-related issues of enormous proportions.
One prior attempt at a solution to the health care problem is called Ask-A-Nurse, wherein a group of nurses provide health information by telephone around-the-clock. A person with a medical problem calls an 800 number and describes the problem to the nurse. The nurse uses a computer for general or diagnostic information on the ailment or complaint mentioned by the caller. The nurse may then refer the caller to a doctor from a computerized referral list for a contracting hospital or group of hospitals. Client hospitals contract with Ask-A-Nurse to provide patient referrals. A managed care option called Personal Health Advisor is similar and adds the capability for the caller to hear prerecorded messages on health topics 24 hours a day.
Several problems exist with these prior medical advice systems. First, these systems have high costs associated with having a nurse answer each telephone call. Second, the caller may have to belong to a participating health plan to utilize the service. Third, and significantly, this system is designed to respond to reactive problems determined by the caller, and therefore, provides no ability to eliminate the possibility of such a condition occurring in the first instance. Fourth, these medical advice systems generally do not possess the requisite in-depth knowledge to provide meaningful guidance in any specific area, e.g., drug use.
Another prior health system provides a computerized service that answers health care questions and advises people in their homes. A health maintenance organization (HMO) may provide this service to its members in a particular geographic area. To get advice at home, an HMO member connects a toaster-sized box to a telephone and calls a toll-free 800 number. Using a keyboard that is part of the box, the user answers questions displayed on a screen of the box relating to the user's symptoms. Depending on the answers, the user might be told to try a home remedy, be called by a nurse or doctor, or be given an appointment to be examined. This system is also designed to respond to reactive problems determined by the caller, and therefore, provides no ability to eliminate the possibility of such a condition occurring in the first instance.
At the other end of the spectrum, are various attempts at analyzing retroactively using hindsight, the appropriateness of the delivered medical care for quality and cost. For example, U.S. Pat. No. 5,544,044 to Leatherman, incorporated herein by reference, relates to a software-based medical information system performs a method of analyzing health care claims records for an enrolled population (e.g., HMO, Medicaid) to assess and report on quality of care based on conformance to nationally recognized medical practice guidelines or quality indicators. FIGS. 1a-1d are flow charts illustrating this software-based medical information system that analyzes health care claims records to assess and report on quality of care.
The process is typically performed at the request of a customer that is a health maintenance organization, indemnity insurer (e.g., Blue Cross), a large, self-insured employer group or a government program (e.g., Medicaid). At the start 1, the first step 3 is to obtain the customer specific parameters, such as what time period the customer wishes to analyze or whether the customer wants to have some data broken down by particular providers or other grouping variables. The next step 4 is to update the system options and parameters using the customer specifications. Thereafter, the system obtains and loads 5 the customer data, usually consisting of the customer's already-computerized health care claims data for a specified period, together with enrollment data and health care provider data.
The enrollment data is extracted 7 so as to identify the enrollees served by the customer that meet a predefined enrollment criterion. The resulting enrollment data 9 contains one record per enrollee. Next, the relevant claims data are extracted 11 from the complete customer data base and are configured through linkages to produce the necessary health records. The claims data will include claims records for medical professional services 12 (outpatient records), claims records for hospital services 13 (inpatient records) and claims records for pharmacy purchases 14 (pharmacy records).
If the customer desires, provider-specific data is also extracted 16 from the customer data, permitting the later analysis to be broken down by the particular provider of services or products, which may be a particular doctor, clinic or hospital. The resulting files are merged 19 to produce uncorrected master files 21. Duplicate claims are excluded 23 and claims that have been reversed through the claims adjudication process. This produces a master file 25 of health care claims records.
Step 29 involves the application of the definitions for the health care condition to identify the population having that condition, followed by an analysis of the claims records for that population (a subset of the master files 25) under the defined quality care criteria. The result of the analysis in step 29 is a report that includes: charts and graphs 31 reporting statistically observed quality of care data in the population defined as having the health care condition of interest; a written analysis reporting, from a care quality viewpoint, statistical results considered worthy of highlighting 33, 35; and a report containing recommendations for actions to improve health care quality 37, 39.
Analysis for multiple health care conditions takes place iteratively through the software at step 41, and the process just described, comprising steps 29, 31, 33, and 37 and producing charts and graphs 31 and reports 35, 39 is repeated, using the next health care condition definition to identify the population having that condition, followed by an analysis of the claims records for that population under the defined quality care criteria for that next condition. After all the specified health care conditions have been processed in this manner, the reports for each condition are assembled 43 into a claims-based quality report 45 that is is presented to the customer 47.
At step 51, the system recognizes whether there is the need for detailed analysis. If no such need exists, no further data collection or analysis occurs. However, if a need for detailed analysis of any health care condition has been determined, then the population identified as having that condition is subjected to sampling 55 to determine for which enrollees additional medical records information will be collected. With the provider's consent, the medical records are abstracted 59 with a particular focus on events that relate to the particular health care condition under study, resulting in a completed medical records abstract form 61.
This abstracted information is then entered into the system 63, via personal computer to produce a medical record abstract data file 65. Charts and graphs reporting statistically observed data in the population defined as having the health care condition of interest 69 and a report containing recommendations for actions to improve health care quality 71, 73. If detailed analysis of medical records is specified for multiple health care conditions, then the preceding steps are repeated until charts and graphs reporting statistically observed data 69 and a report containing recommendations for actions to improve health care quality 71, 73 are developed for each health care condition.
After all the specified health care conditions have been processed in this manner, the reports for each condition are assembled 77 into a detail level report 79 that is presented to the customer 81, and the process ends 83. However, one major drawback of this system is that it analyzes "after-the-fact" the appropriateness of the delivered medical care for quality and cost.
U.S. Pat. No. 5,660,176 to Iliff, incorporated herein by reference, is directed to a computerized medical diagnostic and treatment advice system. Referring to FIG. 2, the components of the computerized medical diagnostic and treatment advice system 100 are shown. A personal computer (PC) 102 includes a plurality of components within an enclosure 104. A plurality of telephone lines 106 interface the public telephone network 108 to the computer 102. One of telephone lines 106 is shown to be switched via network 108 to connect with a telephone 110 that is used by a person desiring medical advice (user) 112.
The system runs on the PC 102 with a microprocessor. Telephony functions use a voice processing board 122 based on a digital signal processor (DSP). A group of one to four telephone lines 106 connect to the VP board 122. The computer 102 may include a plurality of VP boards 122 based on how many phone line connections are desired for the system 100. Speech recognition is achieved using Voice Processing Corporation's speech recognition VPRO-4 board 124 (also DSP based).
The VR board 124 and the VP board 122 both connect to an industry standard architecture (ISA) bus 126. The VP board 122 also connects to a VPRO-Adapt board 128 via an analog audio bus 130 that is called Analog Extension Bus. The Adapt board 128 further connects to a digital audio bus 132. The VR board 124 also connects to the digital audio bus 132. The Adapt board 128 performs analog to digital signal conversion to a VPC-proprietary digital pulse code modulation (PCM) format.
A video adapter board 136, preferably at VGA or better resolution, interconnects to a video monitor 138. A serial communication circuit 140 interfaces a pointing device, such as a mouse 142. A parallel communication circuit may be used in place of circuit 140 in another embodiment. A keyboard controller circuit 144 interfaces a keyboard 146. A small computer systems interface (SCSI) adapter provides a SCSI bus 150 to which a 500 Mb or greater hard disk drive 152 is attached.
The hard drive 152 stores database files such as the patient files, speech files, and binary support files. Main memory 156 connects to the microprocessor 120. An algorithm processor 160 includes a parser and supporting functions that manipulate a memory variable symbol table and a run time stack.
FIG. 3 is a block diagram illustrating a conceptual view of the database files and processes of the system of FIG. 2. Patient login process 250 is used to identify a patient who has previously registered into the system. Art assistant login process 272 is used to identify an assistant who has previously registered into the system. An assisted patient login process 276 is used to identify a patient who has previously registered into the system.
If the caller is the patient, a patient registration process 252 is used to register new or first-time patients. If the caller is not the patient, an assistant registration process 274 is used to register new or first-time assistants. Then, if the patient is not already registered, an assisted patient registration process 278 is used to register the patient.
The master patient and assistant enrollment database 260 is created at run-time by one of the registration processes 252, 274, or 278. This database 260 is read by the patient login process 250 or the assisted patient login process 276 to validate a patient's identity at login time, and by the assistant login process 272 to validate an assistant's identity at login time. The database 260 is essentially a master file of all registered patients and assistants indexed by their patient ID number or assistant ID number, respectively.
In Iliff, the medical advice is provided to the general public over a telephone network. Two new authoring languages, interactive voice response and speech recognition, are used to enable expert and general practitioner knowledge to be encoded for access by the public. Meta functions for time-density analysis of a number of factors regarding the number of medical complaints per unit of time are an integral part of the system. Thus, the system in Iliff is also designed as a reactive measure to respond to caller complaints, and provides no process for ensuring and/or designing appropriate patient care, through the selection and/or collection of extensive information on a patient's use of medication(s), medical history, and/or satisfaction.
U.S. Pat. No. 4,839,822 to Dormond et al., incorporated herein by reference, relates to a computer system and method for suggesting treatments for physical trauma. FIG. 4 is a block diagram illustrating the structure of the expert system. The expert system 201, includes an inference engine and processor 210, inference interface 211, application program 212 and application interface 213. The inference engine and processor 210 functions as in an inference engine, under the control of an inference engine program, and also executes the application program 212, when necessary to perform application program functions, under the overall control of the inference engine.
Communication between the expert system and the user is by way of a CRT/keyboard 215 and through the inference interface 211, for communicating with the inference engine and processor 210, and by way of application interface 213, for communicating with the application program 212. The inference engine and processor 210 receives information from two data bases, namely a knowledge base 216, and a data base of working files 218 which are generated by the application program 212, based on information elicited from the user. A procedures and classification data base 220, input-output graphics 222 and classification graphics 224, are provided for the purpose of gathering the requisite patient and trauma information from the system user, and assembling that information into the working files 218.
In Dormond et al., the application program is executed in the computing device and interactively displays a series of screens including at least some of the graphical illustrations, to elicit responses from the user concerning the specific types of physical trauma and specific characteristics of the patient. The inference engine program, which is also executed in the computing device, uses the knowledge base and information related to the responses elicited from the user, for selecting one or more suggested treatments. The application program presents the suggested treatments to the user after execution of the inference engine program. However, the Dormond et al. system does not generally address issues relating to optimizing, coordinating and/or providing information about medication therapy, for example, when multiple medications are used, nor address coordination of such therapy with the appropriate individuals.
Accordingly, we have determined that it is desirable to provide a method and/or system to optimize or coordinate medication and/or health care therapy, for example, when multiple medications are used and/or when multiple prescribers have been involved.
We have also determined that it is desirable to provide a method and/or system that coordinates medication and/or health care therapy for the appropriate individuals or patients.
We have also determined that it is desirable to provide a method and/or system that proactively determines a patient target population to selectively apply the above medication and/or health care therapy, for better utilization of resources in conducting same.
We have also determined that it is desirable to provide a method and/or system that dynamically or in real-time analyzes the appropriateness of the delivered medication and/or health care therapy for appropriateness, quality and/or cost.
We have also determined that it is desirable to provide a method and/or system that ensures and/or designs and/or coordinates appropriate patient care, through the selection and/or collection of extensive information on a patient's use of medication(s), medical history, and/or satisfaction.
We have also determined that it is desirable to provide a method and/or system that minimizes the possibility of the occurrence of adverse health conditions in the first instance using proactive medication therapy.
We have also determined that it is desirable to provide a computer implemented and/or assisted process for ensuring and/or designing appropriate patient care, through the selection and/or collection of extensive information on a patient's use of, for example, medications, medical history, and/or satisfaction, as well as the involvement of both the patient and physician in the decision-making process.
We have also determined that it is desirable to provide a method and/or system that lowers costs of health care therapy, by dynamically or in real-time analyzing the appropriateness of the delivered medication and/or health care therapy for appropriateness, quality and/or cost, and by reducing fragmentation of prescriptions and/or improving efficiency of drug use.